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Risk prediction models for mortality in patients with cardiovascular disease: The BioBank Japan project
Jun Hata1, Akiko Nagai2, Makoto Hirata3
1Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Insights
New risk prediction models were developed for long-term mortality in patients with chronic cardiovascular disease (CVD). These models utilize traditional risk factors to estimate risks of all-cause and cardiovascular death.
Area of Science:
- Cardiology
- Public Health
- Biostatistics
Background:
- Cardiovascular disease (CVD) is a primary cause of mortality in Japan.
- Chronic CVD patients require accurate long-term risk assessment for mortality.
Purpose of the Study:
- Develop and validate novel risk prediction models.
- Estimate long-term risks of all-cause and cardiovascular death in chronic CVD patients.
Main Methods:
- 15,058 chronic ischemic CVD patients (aged ≥40) from BioBank Japan were randomly assigned to derivation (n=10,039) and validation (n=5,019) cohorts.
- Cox proportional hazards regression was used to develop models based on ten traditional risk factors.
- Model performance was assessed for 5-year mortality risk in the validation cohort over a median follow-up of 8.55 years.
Main Results:
- Models for all-cause and cardiovascular death were derived using age, sex, CVD subtype, hypertension, diabetes, total cholesterol, BMI, smoking, drinking, and physical activity.
- The models showed modest discrimination (c-statistics: 0.703 for all-cause, 0.685 for CVD death) and good calibration in the validation cohort.
- All-cause and cardiovascular death events occurred in 2962/962 and 1536/481 patients in the derivation/validation cohorts, respectively.
Conclusions:
- Validated risk prediction models for all-cause and cardiovascular death in chronic ischemic CVD patients have been developed.
- These models can aid in estimating long-term mortality risk for individuals with chronic CVD.
Background:
Cardiovascular disease (CVD) is a leading cause of death in Japan. The present study aimed to develop new risk prediction models for long-term risks of all-cause and cardiovascular death in patients with chronic phase CVD.
Methods:
Among the subjects registered in the BioBank Japan database, 15,058 patients aged ≥40 years with chronic ischemic CVD (ischemic stroke or myocardial infarction) were divided randomly into a derivation cohort (n = 10,039) and validation cohort (n = 5019). These subjects were followed up for 8.55 years in median. Risk prediction models for all-cause and cardiovascular death were developed using the derivation cohort by Cox proportional hazards regression. Their prediction performances for 5-year risk of mortality were evaluated in the validation cohort.
Results:
During the follow-up, all-cause and cardiovascular death events were observed in 2962 and 962 patients from the derivation cohort and 1536 and 481 from the validation cohort, respectively. Risk prediction models for all-cause and cardiovascular death were developed from the derivation cohort using ten traditional cardiovascular risk factors, namely, age, sex, CVD subtype, hypertension, diabetes, total cholesterol, body mass index, current smoking, current drinking, and physical activity. These models demonstrated modest discrimination (c-statistics, 0.703 for all-cause death; 0.685 for cardiovascular death) and good calibration (Hosmer-Lemeshow χ2-test, P = 0.17 and 0.15, respectively) in the validation cohort.
Conclusions:
We developed and validated risk prediction models of all-cause and cardiovascular death for patients with chronic ischemic CVD. These models would be useful for estimating the long-term risk of mortality in chronic phase CVD.